Data Driven Optimization for Digital Recruitment Marketing: A Pilot Study
Vilen, Christian Daniel (2020)
Lataukset:
2020
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2020121729076
https://urn.fi/URN:NBN:fi:amk-2020121729076
Tiivistelmä
PHZ Full Stack Oy is an IT-consulting organization, which has ongoing recruits for projects. PHZ Full Stack uses multiple different digital channels for recruitment. The objective of the thesis is to optimize the recruitment marketing, in order to get the most applications.
To help PHZ Full Stack Oy to reach its goals, the company has a need to measure and keep track of the recruitment marketing results, optimize budgets and assess’ strategies. As there are many factors and channels in recruitment marketing it has begun to be a problem to keep track of them all. The objective of this thesis project was to maximize the number of job application that PHZ receives from its recruitment marketing efforts and allocated budget.
Recruitment marketing advertisements can be optimized with performance marketing, segmentation and targeting. For the controlling and optimizing budgets of different recruitment marketing platforms the project introduced the Linear Programming method, which will help to measure and budget the results of recruitment marketing through a mathematical model.
In the research I created a prototype of a marketing tool, which automates the budgeting of new advertisement campaigns. Historical data and the theoretical background in this thesis were used to develop the prototype. The prototype was tested with a 3-month period where the prototype tries to predict next months results, by optimizing the budgets based on the unit costs of the applications from multiple channels.
The outcome of the pilot study was a prototype was able to predict the costs per unit and optimize the budgets, so that we were able to get 38% more applications. From 15 to 24 applications per month. New channels can be easily integrated into the tool. The tool is already in use at my client.
To help PHZ Full Stack Oy to reach its goals, the company has a need to measure and keep track of the recruitment marketing results, optimize budgets and assess’ strategies. As there are many factors and channels in recruitment marketing it has begun to be a problem to keep track of them all. The objective of this thesis project was to maximize the number of job application that PHZ receives from its recruitment marketing efforts and allocated budget.
Recruitment marketing advertisements can be optimized with performance marketing, segmentation and targeting. For the controlling and optimizing budgets of different recruitment marketing platforms the project introduced the Linear Programming method, which will help to measure and budget the results of recruitment marketing through a mathematical model.
In the research I created a prototype of a marketing tool, which automates the budgeting of new advertisement campaigns. Historical data and the theoretical background in this thesis were used to develop the prototype. The prototype was tested with a 3-month period where the prototype tries to predict next months results, by optimizing the budgets based on the unit costs of the applications from multiple channels.
The outcome of the pilot study was a prototype was able to predict the costs per unit and optimize the budgets, so that we were able to get 38% more applications. From 15 to 24 applications per month. New channels can be easily integrated into the tool. The tool is already in use at my client.